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1

In
-
situ
Ocean Observing system


1.
Introduction

2.
Elements of in
-
situ observing system

2.1
Tide gauges

2.2
V
oluntary Observing Ships

2.3 S
hips of Opportunity

2.4
Drifting

buoys

2.
5

Acoustic Tomography

2.
6
Repeat Hydrography and Carbon inventory

2.
7

Moori
ngs


2.
8

Argo

2.
9

Gliders


2.
10

CODAR

3.
Basin Scale O
bserving system



Indian Ocean

4. Summary & conclusions


2

In
-
situ Ocean Observing System
s


M. Ravichandran,

Indian National Centre for Ocean Information Services (INCOIS),

Ministry of Earth Sciences,
Pos
t Bag No. 21, IDA Jeedimetla,

Hyderabad, INDIA

500055

ravi@incois.gov.in


Abstract

Ocean Observing system
s

consist of in
-
situ and satellite
based
technique to

detect, track, and
predict changes in physical, chemical, geological and biological
processes.
In
-
situ observing
systems
have

both Eulerian
(based on fixed locations)
and L
a
grangian

(
whose location varies
with time
) systems
. The elements
of in
-
situ observing system in terms of
their principle
,
capability to observe the ocean, technology and
some of
th
e applications
pertain
ing

to
physical
variables

are described.
A brief status on Indian Ocean Observing system (IndOOS) is also
described.
The strengths and weakness
es

of each platform and the need for integrating different
observational platforms/sensors
are highlighted.


1.


Introduction

The knowledge of the ocean is
essential

for many sta
k
e
holders

dealing with climatology,
fisheries, ports and harbours, coastal zone management,
n
avy and
c
oast Guard

organizations
,
p
ublic health

institutions
, environment
al

ag
enc
ies
, tourism

industry
, weather forecasters,
offshore mining and oil industr
ies

and
climate
research.
Ocean observing system
s

has

a central
role to deliver ocean services to
the
society. However, data produced by these
systems

need to
be translated into

ocean information
services
by analysis systems and also

assimilate
d

in

ocean
general circulation
models to deliver past, present and future state of
the
ocean and
also
different
products required by user agencies.
A d
istributed or centralized data manag
ement system is
critical to timely delivery of Ocean services.
Ocean observation systems consis
t of (a) in
-
situ
measurements,
using sensors mounted on ships, bu
oys, moorings, coastal stations

to capture
changes in time and depth at specific points or track
s
and (b) remote sensing systems such as
satellites, aircraft, radar
, etc

to capture the spatial and temporal variations synoptically, as
manifested

at the su
rface
.
Remote sensing in general and satellite measurements in particular
(Le Traon, P. Y., this
volume)
provide horizontal distribution of surface variables, such as

3

temperature, sea surface height, ocean color, as well as several meteorological parameters for the
calculation of air
-
sea momentum, heat and fresh water fluxes (Masumoto,
et.al,
2009). T
hese
satellite data enable studies of phenomena across a very wide range of time scales, from
intraseasonal to decadal

and
complement the

in
-
situ observing system
s
.



Ocean observations also
help answering some

fundamental research questions, such as iden
tified
by N
ational
S
cience
F
oundation (NSF
)

reports

(NSF, 2001, Kob
l
i
nsky and Smith,
2001
). They
are (
a
) determining

the role of ocean on climate

and climate change
, (b) quantifying the
exchange of heat, water, momentum and gases between the ocean and atmo
sphere, (c)
determining the cycling of carbon in the oceans and the role of the oceans in moderating the
increase in atmospheric carbon dioxide, (d) improving models of ocean mixing and large
-
scale
ocean circulation, (e) understanding the patterns and cont
rols on biological diversity in the
oceans, (f) determining the origin, development and impact of episodic coastal events such as
harmful algal blooms, (g) assessing the health of the coastal ocean
,

(h) determining the nature
and extent of microbial life
in the deep crustal biosphere, (i) studying subduction zone thrust
faults that may result in large, tsunami
-
generating earthquakes and (j) improving models of
global earth structure and core
-
mantle dynamics.


Climate research became a major focus of scien
tific
debate/
discusstion
by

the
latter

half of the
20
th


century, especially after the identification of the impact of green house gases and global
warming on Earth’s climate system. Many countries, both the developed and developing ones,
are spending cons
iderable amount of their resources for climate research
so
that government
s

and society
can take

appropriate steps in planning and development. A sustained observation
program to detect, track, and predict changes in physical, chemical, geological and bi
ological
systems and their effects is needed to measure the impacts of humans on the ocean as well as the
impact of the human
activity.

The ocean, comprising over seventy percent of the surface of the
planet, is currently monitored far less effectively an
d completely than terrestrial systems
,

yet
humans depend strongly on the sea as a source of food and for transportation and trade, among
many other uses. Further, the ocean strongly affects large
-
scale weather patterns, such as
El
-
Ni
ñ
o
and Sothern Oscillat
ion (
ENSO
)
,
Indian Ocean Dipole (
IOD
)
, etc. In order to understand and
ultimately predict how the ocean
-
atmosphere interaction affects weather and climate, and how

4

human activities affect both the physical system and living marine resources, an integrated
ocean
observing system is needed to monitor the 'state' of the ocean. Just as continuous measurements
of weather and climatic conditions are maintained on land, similar
ly

sustained measurements of
the ocean are required to monitor change and to assist in u
nderstanding and predicting its
impacts.


There are two different classes of
in
-
situ

observing
system
s

-

those
based on

fixed points
(
Eulerian
)

and those whose location varies with time

(
Lagrangian
)
. Fixed point observations

are
made either from moorings o
r from repeated occupation of stations. Observations whose
location
varies

with time are made from platforms that move as a result of the motion of the ocean or of a
moving vessel. Some moving platforms are thought to follow the motion of water parcels fai
rly
well
.

Successful operation of a
global
in
-
situ

observing system

requires that there be
coordination of activities on a number of levels. Sensors and best practices
learned from other
experiences
need to be agreed. Deployment opportunities need to be id
entified and instruments
delivered to take advantage of them; where no opportunistic deployment is feasible, timely
provision of special deployment efforts needs to be made. The data coverage of the system needs
to be monitored along with sensor lifetimes
and provision made to anticipate where gaps will
appear so that deployment can be arranged. Successful implementation depends fundamentally
upon near
-
real time transmission of both observations and relevant metadata. Given that a
number of nations particip
ate in each of the observing networks and both ‘operational’ and
‘research’ programs are involved, this monitoring/system management function is non
-
trivial and
critical

(C
lark

and Wilson, 2009)
.


Though some of the
ocean
processes can be
addressed

and de
scribed using local observations,
many processes need to be addressed using observations from other locations since remote
forcing may play an important role.
Accounting for remote forcing effects would require
observing all basins.

But no country can affo
rd to have observations in all basins.
Hence, many
national and regional programs are networked through
the
U
nited
N
ations
.
The
Global Ocean
Observing System (GOOS) is a
n

oceanographic component of Global Earth Observing System
of Systems (GEOSS)
.

It
is

a system of programmes, each of which is working on different and
complementary aspects
,
for

establishing

an ocean

observation capability for all of the world's

5

nations. UN sponsorship and UNESCO assemblies assure that international cooperation is
always t
he first priority of the Global Ocean Observing System. GOOS is designed to (i)
monitor, understand and predict weather and climate, (ii) describe and forecast the state of the
ocean, including living resources, (iii)
i
mprove management of marine and coast
al ecosystems
and resources, (iv)
m
itigate damage from natural hazards and pollution, (v)
p
rotect life and
property on coasts and at sea and (vi) enable scientific research.

GOOS

is sponsored by
the
Intergovernmental Oceanographic Commission (
IOC
)
,
the
United Nations Environment Program
(
UNEP
)
,
the
World Meteorological Organisation (
WMO
)

and

the

International Council for
Science (
ICSU
)
, and implemented by
m
ember states via their government agencies, navies and
oceanographic research institutions working together in a wide range of thematic panels and
regional alliances.

More detail about GOOS can be found
at
http://www.ioc
-
goos.org/
.

The Joint
Technical Commission for Oceanography and Marine Meteorology (
JCOMM
) of the WMO and
IOC provides coordination at the
i
nternational level for oceanographic and marine observations
from all in
-
situ observ
ing

systems.

The
present
status of location of
different elements of

in
-
situ
observing system i
s available at
http://wo.jcommops.org/cgi
-
bin/WebObjects/JCOMMOPS
.


An i
n
-
situ observing system consists
many elements such as
tide gauges,
ship based marine
meteorology from V
oluntary Obse
rving Ships(VOS)
, S
hips of Opportunity (S
OOP
)

based
XBT/XCTD sections, repeat hydrography,
drifting and moored buoys,
a
coustic tomography,
a
rgo profiling floats,
g
liders, etc. Each
element

has some advantages and disadvantages in terms
of temporal and spa
tial resolutions. Integrating all the
element
s
, sustaining and improving the
different components of observing
system to meet the evolving needs
for societal benefits is a
n
imperative need for ocean observing system.

Though the sensors used in the
se

platf
orms/elements
records
primarily physical variables, the time has come to have multi
-
disciplinary approach to understand the total system. In the following

sections
, the elements of
different

observing system
s

pertaining to physical variables
are explained
in terms of its
capability to observe the ocean, technology and
some of
its applications. The implementation
plan for one of the poorly observed Indian Ocean is

briefed in section.
3
.
The strengths and
weaknesses of each platform and the

final concluding
remarks
emphasizing the

requirement of
optimal mix of different in
-
situ platforms to deliver meaningful information

are presented in
section 4.


6



2.
Elements of Observing system

2.1
Tide gauges

The measurement of changes in sea level to understand the m
echanisms responsible for
phenomena such as the tides and the catastrophic floods due to storms and tsunami was
performed

by the observers of
the
Ocean from ancient times. It is now realized that sea level
changes are important on all timescales from seco
nds (due to wind waves) through to millions of
years (due to the movement of continents). The devices employed to make sea level changes
(relative to the level of the land where the instrument is located) are usually called tide gauges.

It
is based

on
t
he principles of well
-
known float gauge in a stilling well,

the measurement of
sub
-
surface pressure, or of the time
-
of
-
flight of a pulse of sound, or of a pulse of radar.
The
classical and most reliable method of measuring the sea level
is by tide staff,

but it is

prone to
manual errors. Subsequently, the float based tide gauges have been used extensively for long
time. However, such systems require supporting structures, shelters and regular maintenance.
The other

commonly used types are pressure sensor
gauges (differential/absolute) in which
sensors are mounted directly in the sea.

However, this require
knowledge of atmospheric
pressure (in case of absolute pressure sensor), seawater density and gravitational acceleration to
make the conversion from pre
ssure to sea level.

In spite of the above lacuna, the instruments
have many practical advantages as sea level recorders. In late 1990s, radar devices, which were
mainly used in process technology, were introduced into hydrometry.
Though satellite based
alt
imeter provides mean sea level anomaly in the open ocean with coarse temporal resolution
, the
information from gauges is essential for understanding local mean sea level trends and extremes.
Also, gauges data are required to provide precise calibration of
radar altimetry
.

A
part from
this,
tide gauges

ha
ve

a long history and healthy future (IOC

manual
, 200
6
)

with
many
applications

both in operational and scientific research.


The observed sea level consists of
periodic geophysical forces such as
mean sea le
vel,
a
tid
al
signal

and meteorological residuals. Each of these components is controlled by separate physical
processes and the variations of each part are essentially independent of the variations in the other
parts. Tides are the periodic movement of the

seas which
have
coherent

amplitude and phase

7

relationship to some periodic geophysical force. The dominant forcing is the variation in the
gravitational field on the surface of the earth due to the regular movements of the earth
-
moon
and earth
-
sun systems
. These cause gravitational tides. There are also weak tides generated by
periodic variations of atmospheric pressure and on
-
shore
/
off
-
shore winds which are called
atmospheric tides.
Meteorological residuals are the non
-
tidal components
of sea level
which
remain after removing the tides by analysis. They are irregular, as are the variations in the
weather. Sometimes the term

surge residual


is used but more commonly surge is used to
describe a particular event during which a very large non
-
tidal component
is generated. Mean sea
level is the average level of the sea, usually based on hourly values taken over a period of at least
a year. For geodetic purposes the mean sea level may be taken over several years. More elaborate
techniques of analysis allow the e
nergy in seal level variations to be split into a series of
frequency or spectral components. The main concentration of energy is in the semidiurnal and
diurnal tidal bands, but there is a continual background of meteorological energy which becomes
more im
portant for longer periods or lower frequencies.


The
Global Sea Level Observing System (
GLOSS
)

(
http://www.gloss
-
sealevel.org/
) was
established in 1985 by
IOC
to provide oversight and coordination for global
and regional sea
level networks in support of oceanographic and climate research. GLOSS remains under the
auspices of the IOC and is one of the observing components
of

JCOMM.
GLOSS is an example
of a global coastal observing network and
has the largest p
articipation of member states
(~70)

among the existing observing elements in GOOS
. Tide gauge data from the GLOSS networks
are assembled and archived at two data centers (
Merrifield
,

et al
.
,

2009). The British
Oceanographic Data Center (BODC, http://www.
bodc.ac.uk/) is responsible for delayed mode
datasets
. T
he main archive for historic, monthly
-
averaged, sea level records from tide gauges
from around the world
is available at
Permanent Service for Mean Sea Level (PSMSL,
http://www.pol.ac.uk/psmsl/)
(Wood
worth and Player, 2003).
Fig.
1

shows the present status of
reporting of the sea level gauges in the GLOSS Core Network

(
Merrifield
et al
.
, 2009)
.


Estimates of twentieth century sea level rise are primarily based on the historical tide gauge data
mainta
ined by the
PSMSL
.

Church
et al
.,

(2004) estimated monthly distributions of large scale
sea level variability and change over the period 1950
-
2000 using historical tide gauge data and

8

altimeter data sets.
Annual averages of the global mean sea level (mm
) a
s

derived from analyses
of tide gauges
shows a
global rise of 1.8 ± 0.3 mm/yr during 1950 to 2000
.
Tide gauges have
also
been used to monitor the stability of satellite altimeter sea surface height observations,
long
term
sea level
trends at
coastal

statio
ns
, navigation, hydrography, flood warning, tsunami
warning and other coastal engineering applications.


2.
2

V
oluntary Observing Ship
s

The
Voluntary Observing Ship
s

(VOS)
s
cheme is an international programme comprising
member countries of the WMO/IOC that
recruit ships to take, record and transmit marine
meteorological observations whilst at sea. The
VOS Scheme

is a core observing programme of
the
Ship Observations Team

(SOT) in the Observations Programme Area of JCOMM.
There are
three types of ships in th
e
VOS Scheme

such as
s
elected ships,
s
upplementary ships and
a
uxiliary ships. A selected ship is equipped with sufficient certified meteorological instruments
for making observations, transmits regular weather reports and enters the observations in
meteoro
logical logbooks. Most of the
VOS

are selected ships. A supplementary ship is equipped
with a limited number of certified meteorological instruments for making observations, transmits
regular weather reports and enters the observations in meteorological lo
gbooks. An auxiliary
ship is without certified meteorological instruments and transmits reports in a reduced code or in
plain language, either as a routine or on request, in certain areas or under certain conditions.
Auxiliary ships usually report from dat
a
-
sparse areas outside the regular shipping lanes.


Currently, VOS typically report every six or three


hours

interval
, and make observations of
surface wind speed and direction, air temperature, humidity, sea surface temperature (SST),
atmospheric sea lev
el pressure (SLP), cloud (including type, amount and height), wave and swell
parameters and weather (including visibility) information. The data are sent to a meteorological
service as soon as they are
obtained
, either by radio telephony to a coast
al

radio

station, by telex
over radio, or by
INMARSAT
-
C
. Around 5000 ships are
presently
reporting marine
meteorological parameters. Observations, such as sea ice and precipitation can also be

reported.
The temperature (air and SST), humidity and SLP are
measured
in
-
situ

by meteorological

instruments, whilst waves, clouds and weather types are estimated visually. Wind reports are a
mixture of measurements and visual estimates. The observations
are transmitted in real time and

9

also recorded in paper or, with increasing frequency, electronic logbooks. The electronic logbook
software is also used to format manual observations,
calculate
more uniformly (e.g. dewpoint,
true wind) and perform simple
quality control. (
Elizabeth
Kent
et al
.
,
2009
)
.


Automated weather stations (AWSs) are being installed on VOS in increasing numbers, resulting
in more frequent observations. However
,

a systematic programme of intercomparison with the
traditional observatio
ns to ensure data continuity in keeping with GCOS monitoring principles is
presently lacking. Moreover, a full high
-
quality AWS is expensive and some national services
install low cost systems making only a subset of the normal range of observations, typic
ally SLP
and one or two other variables. Some elements of the VOS report require manual input, typically
the visual estimates. Convincing the observers that supplementing the reports with this vital
information is worthwhile has proved challenging, and the

introduction of AWSs has led to a
marked decline in the proportion of reports containing these parameters. Adding the capability
for manual input adds to both the cost and complexity of the systems and is not always judged to
be cost
-
effective
.


The surf
ace

ocean

observing system has evolved rapidly over the past half century, from being
primarily VOS
-
based through the 1960s, to comprising increasing numbers of moored and
drifting buoy observations starting in the 1970s and particularly dominating the las
t decade.
Elizabeth
Kent
et al
.,

(2009)
show an example

of how the number of
in
-
situ

observations

available in

the

International Comprehensive Ocean
-
Atmosphere Data Set (ICOADS) has
changed over time for selected variables, with the impact of the drifting

buoys clearly visible
for
SST. These
in
-
situ

observations have been complemented by satellite measurements th
at began
in the late 1970s.

To meet the needs of applications such as weather forecasting, VOS
observations are transmitted in real time to
the

Na
tional Meteorological and Hydrological
Services (
NMHS
s)

who then share the observations with other services using the
Global
Telecommunications System (
GTS
)
. Some NMHSs keep an archive of the data extracted from
the GTS, however, these can differ between s
ervices due to differences in data conversion and
storage formats and the way in which the data are retrieved from the GTS.

VOS data contain
fairly large random uncertainties, but in many regions the mean uncertainty due to poor sampling
is much larger (Ke
nt and Berry 200
8
, Gulev
et al
.
,

2007). In well
-
sampled regions the random

10

uncertainties in gridded datasets will be small as many observations can be averaged. Sampling
by multiple platforms gives the potential for extensive quality assurance, including n
ear
neighbour “buddy checks” and analysis of outliers. Typically VOS grid box averages contain
observations from multiple platforms, allowing random uncertainty and also ship
-
to
-
ship biases
to be reduced by the averaging process.


Datasets and analyses ba
sed on ICOADS are highly cited in the literature and form an important
resource for climate researchers, especially those interested in large
-
scale estimates of ocean
-
atmosphere exchange of heat, freshwater and momentum and multidecadal climate variability
.
Datasets using VOS observations

in many cases based on the ICOADS collection

includ
e

SST, sea level pressure,

air temperature and humidity, surface fluxes and surface waves. In
addition, it should be noted that atmospheric model reanalyses, which are w
idely used for
climate analysis, are heavily dependent on the assimilation of ship observations (Trenberth
et al
.
,
2009
).

National and international assessments of climate change, most prominently by the
Intergovernmental Panel on Climate Change (IPCC), us
e VOS SST data in the assessment of
global mean surface temperature changes. Confidence in the SST trend is increased by its
consistency with the marine surface air temperature trend, which is an independent
measurement.
VOS are the major source of air tem
perature information over the ocean

and

also contribute to
the monitoring of climate change, for example in the bias
-
adjustment of infrared satellite
estimates of SST (e.g., Reynolds
et al
.
,

2005). VOS also provide a consistent record of cloud
changes sinc
e 1949 and have been used to derive a century
-
long analysis of wave information.
The continuing move to produce data products in a timely manner from VOS data should allow
an enhanced climate monitoring role for the VOS, if sampling can be maintained or i
mproved.
However,
VOS datasets are currently underutilized for calibration and validation. New higher
-
resolution datasets characterised by uncertainty estimates should have wide applications for
calibration and validation.


2.3 S
hips of Opportunity

The p
rimary
objective

of the Ship
-
of
-
Opportunity Programme (SOOP) is to fulfill
the XBT
upper ocean data requirements established by the international scientific and operational
communities, which can be met at present by measurements from ships of opportunity.

The

11

annual assessment of transect sampling is undertaken by the Joint WMO
-
IOC Technical
Commission for Oceanography and Marine Meteorology (JCOMMOPS) on behalf of
the Ship
Of Opportunity Programme Implementation Panel (SOOPIP).
Data management is taken ca
re of
through the Global Temperature Salinity Profile Programme (GTSPP)

( Goni, et.al, 2009).

The
SOOP is directed primarily towards the continued operational maintenance and co
-
ordination of
the XBT ship of opportunity network but other types of measureme
nts are
also
being made (e.g.
TSG, XCTD, CTD, ADCP, pCO2, phytoplankton concentration). This network in itself supports
many other operational needs (such as for fisheries, shipping, defense, etc.) through the provision
of upper ocean data for data assimil
ation in models and for various other ocean analysis schemes.
One of the continuing challenges is to optimally combine upper ocean thermal data collected by
XBTs with data collected from other sources such as
mooring

array
s
, Argo, and satellites (eg.
AVHR
R, altimeter, etc.). However, it is considered most important to have the SOOP focused on
supporting climate prediction in order to ensure the continued operation of the present network.


XBT (Exp
e
ndable BathyThermograph) is an exp
e
ndable temperature and
depth profiling system.
It is typically comprised of an acquisition system onboard the ship, a launcher, and a expandable
temperature probe.
The f
alling probe is linked to the acquisition system through a thin insulated
conductive wire which is used to tra
nsmit the temperature data back to the acquisition system in
real time. Depth is deduced from elapsed time using a well calibrated fall rate equation (about
6.5 m/s). Processed profile data can be transmitted in real
-
time through satellite.
The real
-
time
d
ata is being archived at Coriolis data center
, Brest, France

and the delayed mode data at GTSPP
managed by NOAA/NODC.
Profiles as deep as 1000 m and comprising (T, D) data points every
meter can be made although with usual probes depths range from 500 to 8
00 m. Accuracy is
normally better than 5 m for depth, and better than 0.05
º

C for temperature.
The
Global XBT
network containing OceanObs99 recommendations and current proposed transects recommended
in OceanObs’09
is

shown in Fig.
2
.


The scientific and
operational communities deploy approximately 23,000 XBTs every year. In a
typical year
,

50% are deployed in the Pacific Ocean, 35% in the Atlantic Ocean and 15% in the
Indian Ocean. Profiles from about 90% of the XBT deployments are transmitted in real
-
tim
e,
which represent around 25% of the current real
-
time vertical temperature profile observations

12

(not counting the continuous temperature profiles made by some moorings). XBT operates three
modes of deployment: (a) High Density (HD) : 4 transects per year,

1 XBT deployment every
approximately 25 km (35 XBT deployments per day with a ship speed of 20kts), (b) Frequently
repeated (FR): 12
-
18 transects per year, 6 XBT deployments per day (every 100
-

150 km) and
(c)
Low Density(LD)
: 12 transects per year, 4 XBT

deployments per day.


The HD transects extend from ocean boundary (continental shelf) to ocean boundary, with
temperature profiling at spatial separations that vary from 10 to 50 km in order to resolve
boundary currents and to estimate basin
-
scale geostr
ophic velocity and mass transport integrals.
PX06 (Auckland to Fiji), which began in 1986, is the earliest HD transect in the present network
with more than 90 realizations. Some transects are being assessed for their contribution in this
mode. For exampl
e, the CLIVAR IOP noted that further work is required to assess the value of
IX10, which transects the openings of the Bay of Bengal and the Arabian Sea. Scientific
objectives of HD sampling and examples of research targeting these objectives are outlined
in
Goni,
et. al.,
(
2009
)
.


The FR transects cross major ocean current systems and thermal structure
s
. In some cases, for
currents near a continental boundary an extra profile
that crosses

the 200 m depth contour
is
made
to mark the inshore edge of the cur
rent. The FR transects are selected to observe specific
features of thermal structure (e.g. thermocline ridges), where ocean atmosphere

interaction is
strong. Estimates of geostrophic velocity and mass transport integrals across the currents are
made by lo
w pass mapping of temperature and dynamical properties on the section. The proto
-
types of FR transects are IX01 and PX02, which now have time series extending more than 20
years. The earliest transect from Fremantle to Sunda Strait

(
Indonesia
)

began in 198
3
,
has been
sampled at 18 times per year
after
1986. IX01 crosses the currents between Australia and
Indonesia, including the Indonesian Throughflow and has been used in many studies of the
Throughflow and the Indian Ocean Dipole.
The FR sampling produces
well resolved monthly
time series of thermal structure along transects. Using IX01, Meyers
et al
.,

(1995) shown the
mean thermal structure
g
enerally westward flow in the deeper part of the thermocline and
eastward shear in the

shallow (<150 m)
layer.

Als
o, brought out
the strongest

variability in
temperature is at the northern end of the transect near Indonesia.

The temperature sections were

13

used to understand the relationship of interannual variation in transport of Indonesian
Throughflow to
ENSO

(
Meyers
, 1996
)
.
Further, the

time
-
variation of temperature at the north
end of IX01 clearly shows the strong, subsurface upwelling associated with the start of the IOD
events of 1994 and 1997, before the start of surface cooling. These and the other FRX time seri
es
have been used to understand how subsurface thermal structure varies across the Indian Ocean
during
IOD

events
(
e.g. Rao
et al
.
,

2002; Feng and Meyers, 2003
)
.
The u
se of FR lines in the
Indonesian region to study the Indonesian Through
-
flow is discussed

in the Indian Ocean white
paper
(
Masumoto
et al
., 2009
)
.


Low density transects have both operational and scientific objectives, such as investigate
intraseasonal to
i
nterannual variability in the tropical oceans, measur
ing

temporal variability of
boundar
y currents, and investigat
ing the

historical relationship between sea
surface
height and
upper ocean thermal structure. Many illustrative examples of applications of XBT observations,
primarily from LD mode, are presented in
the
XBT white paper
(
Goni
et

a
l
.
,

2009).


2.
4

Drifting Buoys

For several years, oceanographer
s

and meteorologists have deployed satellite
-
tracked drifting
buoys in support of their research and operational programmes. These two bodies of users have,
however, sought different capabiliti
es from their drifters: the oceanographers have mainly looked
for designs which accurately follow water parcels at a given depth, whereas the meteorologists
have equipped their drifters with air
-
pressure sensors to collect real
-
time observations for
weathe
r forecasting. Despite efforts by both user groups to develop combined programmes, these
two main requirements have been largely incompatible, particularly in respect to the size and
above
-
surface exposure of the drifter. The success of the low
-
cost WOCE S
urface Velocity
Programme (SVP) oceanographic drifter, with its accurately quantified water following
characteristics and proven longevity, prompted renewed interest in the development of a low cost
met
-
ocean drifter capable of satisfying the needs of both

user communities. The result is the SVP
Barometer (SVP
-
B) drifter, whose design and use is described
in
the
DBCP Report

(
Sybrandy
et
al
., 2009)
.
This design, refined over several years and after extensive testing, further develops
the original SVP drifter

by inclusion of a novel barometer port. This inexpensive but stable

14

pressure sensor combined with a data filtering algorithm removes pressure spikes resulting from
the repeated immersion of the drifter by waves
.



Drifting buoys normally measure sea
surfa
ce
temperature

(SST)

and air pressure, and by tracking
their positions the surface currents

(resultant current arising from Ekman and geostrophic)

can be
determined. Some drifters also have sensors to measure wind
, temperature profile

and salinity.
The buo
ys are battery powered and typically last for one to two years.
The buoys are disposable
and can be deployed at sea by regular ship crews.
Measurements are normally made hourly and
the data are transmitted
by

satellite. Most drifters use the ARGOS satellit
e system for data
transmission and positioning, although new systems such as Iridium are currently being
evaluated
as a

pilot
programme
.

At present, users can access web pages at both ISDM
(
http://www.meds
-
sdmm.dfo
-
mpo.gc.ca/isdm
-
gdsi/drib
-
bder/index
-
eng.
htm
) and AOML
(
http://www.aoml.noaa
.gov/phod/

dac/gdp.html
)
,

where products and data are available
.
Integrated Science
D
ata
M
anagement (
ISDM) in Canada became a Responsible National
Oceanographic Data Centre (RNODC) for

Drifting Buoy Data on behalf of
JCOMMOPS. The
present status of global drifters as on Nov, 09, 2009 is shown in Fig.
3
.


Drifting buoys, along with Voluntary Observing Ships, provide the primary source of air
pressure data over the oceans that are needed

to run global and regional weather forecasting
models.
The SST data provided by drifting buoys are
important for climate data sets.
The key
application of surface drifter data is reduction of the bias error in satellite SST measurements,
mapping large sc
ale surface currents and identifying their role in heat transports and the
generation of SST patterns and variability.

They are
invaluable as an independent
validation
tools for model and satellite derived currents (ekman + geostrophic), synthesized with a
ltimetry
and satellite winds to estimate absolute sea
surface
height (
Niiler, et.al, 2003, Rio and
Hernandez, 2003) and used to understand the role of surface transport in the genesis of the El
Ni
ñ
o (Picaut et.al, 2002, Lagerloef et.al, 2003, McPhaden, 200
4).

Maximenko
et al
.

(2008)
compared the drifter
-
sensemble averaged velocities and a sum of time averaged geostrophic and
Ekman currents, and concluded that one drifter per 5 x 5 deg grid is not adequate to capture
/resolve most of the
surface current
fea
tures.


Dohan
et
al
.

(2009) describes the data quality from
drifters and the principal scientific insights during the last decade.
There are numerous direct

15

uses of sea surface velocity, such as for navigation and drift trajectories, advection calculations

of ocean properties, spills, and Synthetic Aperture Radar (SAR) operations, etc.
Also, d
rifting
buoy data are used

for many applications such as
to study physical characteristics and
climatology of sea ice within the Antarctic sea ice zone. These data are

also used for

many
applications such as

to trace the seasonal pathways of freshwater plumes ( Sengupta et.al, 2006),
improving the surface current climatology (Shenoi et.al ,1999)
, etc,.


2.
5

Acoustic Tomography

The ocean is largely transparent to sound,

but opaque to electromagnetic radiation. Underwater
sound is therefore a powerful tool for remote sensing of the ocean interior.
This technique is used
in
Ocean

Acoustic
Tomography
. It is used to measure
temperatures

and currents over large
regions of the ocean

(Walter et
.

al 1995)
.

On ocean basin scales, this technique is also known as
acoustic thermometry. The technique relies on precisely measuring the time it takes sound
signals to travel between two instrum
ents, acoustic source and a
receiver
,
by distance within the
range of 100
-
5000 km. If the locations of the instruments are known precisely, the measurement
of time
-
of
-
flight can be use
d to infer the speed of sound, averaged over the acoustic path.
Changes in the
speed of sound

are primarily caused by changes in the temperature of the ocean;
hence the measure
ment of the travel times is equivalent to a measurement of temperature. A 1°C
change in temperature corresponds to about 4 m/s change in sound speed. An oceanographic
experiment employing tomography typically uses several source
-
receiver pairs in a
moored

array
that measures an area of ocean.
Sound is widely used for remote sensing of the ocean on small
scales (e.g., acoustic Doppler current profilers), but a
coustical measurements have been
underexploited in regional and global ocean observations relative to
in
-
situ

instruments and
electromagnetic radiation

(Dushaw
et al
., 2009)


T
his technique integrates temperature variations over a large region hence the sm
aller scale
turbulent and internal
-
wave features that usually dominate point measurements are averaged out
and we can better determine the large
-
scale dynamics. For example, measurements by
thermometers

(i.e., moored or
Argo

floats) have to contend with this 1
-
2 °C noise, so that large
numbers of instruments are required to obtain an accura
te measure of average temperature. For
measuring the average temperature of ocean basins, therefore, the acoustic measurement is quite

16

cost effective. Tomographic measurements also average variability over depth as well, since the
ray paths cycle throughou
t the water column.


Basinwide and regional tomography were accepted as part of the ocean observing system by
OceanObs’99 (Koblinsky and Smith 2001, Dushaw
et al
.
,

2001). Since then, a decade of
measurements of basinscale temperature using acoustic thermom
etry have

been completed in the
North Pacific Ocean. In this project
a
coustic sources located off central California (1996
-
1999)
and north of Kauai (1996
-
1999, 2002
-
2006) transmitted to receivers distributed throughout the
northeast and
north central Pacif
ic
.

The
result shows

that t
he interannual, seasonal, and shorter
period variability was large; as compared to the long term decadal trends.
Acoustic travel
-
time
data have been used previously in simple data assimilation experiments, and they can now be
co
mpared to assimilation products from state
-
of
-
the
-
art models from the ECCO (Estimating the
Circulation and Climate of the Ocean) Consortium. Not surprisingly, comparisons between
measured
travel times and those predicted using Ocean models, constrained by
satellite altimeter
and other data show
significant similarities and differences. Measured acoustic travel times have
uncertainties much less than the differences between two model implementations by the ECCO
Consortium. The acoustic data ultimately need t
o be combined with upper
-
ocean data from A
rgo
fl
oa
ts

and
,

sea surface height data from satellite altimeters to detect changes in abyssal ocean
temperature and to quantitatively determine the complementarity of the various data types
(Dushaw
,

200
3
)
.


Apart
from this
,

the passive

acoustics can be used for a variety of purposes such as: tracking,
counting and studying the behavior of vocalizing marine mammals and fish; assessing and
monitoring the ecological impacts of ocean warming and acidification on marine

ecosystems and
biodiversity; detecting nuclear tests; detecting and quantifying tsunamis; measuring rainfall

(Riser
et al
.
, 2008)
; measuring the properties of undersea earthquakes (e.g., de GrootHedlin
2005) and volcanoes; monitoring the sound produced b
y high

latitude sea ice; monitoring
anthropogenic activities in marine protected areas

and also in commercial use.
The acoustic
measurements supporting these projects can be real time and provide information about local
ambient noise sources such as shippi
ng, wind, rain, as well as noise from offshore wind farm
s
.



17

2.6

Repeat hydrography and carbon inventory

Despite numerous technological advances over the last several decades, ship
-
based hydrography
using research vessel
remains the only method for obtaining hi
gh
-
quality, high spatial and
vertical resolution measurements of a suite of physical, chemical, and biological parameters over
the full water column (Hood, et.al,. 2009).
It is worth mentioning here that VOS and SOOP
collect data while cruising, whereas re
search vessel stops at different locations and collect
surface and subsurface data upto full vertical resolution.
Ship
-
based hydrography is essential for
documenting ocean changes throughout the water column, especially for the deep ocean below 2
km (52% o
f global ocean volume). Hydrographic measurements are needed to (a) reduce
uncertainties in global freshwater, heat, and sea
-
level budgets, (b) determine the distributions and
controls of natural and anthropogenic carbon (both organic and inorganic), (c) d
etermine ocean
ventilation and circulation pathways and rates using chemical tracers, (d) determine the
variability and controls in water mass properties and ventilation, (e) determine the significance of
a wide range of biogeochemically and ecologically i
mportant properties in the ocean interior, and
(f) augment the historical database of full water column observations necessary for the study of
long
-
timescale changes.


Shipboard hydrographic data provide the quality standard against which the data from f
loats and
other autonomous platforms and XBTs are compared, to assess their accuracy and for detection
and correction of systematic errors. The high cost of shipboard hydrography is balanced against
its broad and unique capability to measure many parameter
s that cannot be measured by other
means, and to measure those that can with highest accuracy. Cost factors limit the global
hydrographic survey to less than 103 profiles per year from the ocean surface to the bottom,
while Argo floats deliver 105
thousand

temperature/salinity profiles per year in the upper 2 k
m.
The recommended hydrographic sections for the sustained decadal
survey
is

shown in Fig.
4
.


D
ue to the increasing amount of Argo profiling floats in the ocean and to their moving nature,
Argo floa
ts can not be recovered and their sensors can not be recalibrated at the end of their
lifetime. The main problem concerns the conductivity sensor that may drift or show an offset due
to biological fooling and other problems. To ensure the quality of data,
salinity drift in the
conductivity sensors are adjusted by comparison of Argo salinity to nearby high
-
quality salinity

18

/temperature data (Wong and Owens, 2009).
In addition to salinity drift, systematic errors in
float pressure measurements are also an on
going concern (e.g. Willis
et al
.
,

2008). For both of
these

issues, the process of identifying and correcting systematic errors is dependent on, and its
effectiveness is limited by, the volume and spatial distribution of recent shipboard CTD profiles.
The
requirements have not yet been established for high quality reference CTD data needed to
validate and correct Argo. Similarly, shipboard CTD data are used to assess systematic changes
over time in temperature versus depth errors from XBTs, for example to e
stimate and adjust the
instrument’s fall rate (Wijffels
et al
.,
2008). Other parameters are required to be collected since
future Argo floats are likely to carry sensors for dissolved oxygen, chlorophyll
-
A, particulate
organic carbon, and possibly others.


The CLIVAR and Carbon Hydrographic Data Office (CCHDO) is the repository and distribution
center for global CTD, hydrographic, carbon, and tracer data of the highest quality.These data
are a product of WOCE, CLIVAR, the International Ocean Carbon Coordina
tion Project
(IOCCP), and other oceanographic research programs
-

past, present and to come.


Hydrographic
data acquired by investigators are pooled, verified, assembled and disseminated to users in
different format
s
. The CCHDO’s primary window to the res
earch community is via its web site
(
http://cchdo.ucsd.edu
).


2.
7

Moorings

Moorings are capable of measuring some of the key variables needed to describe, understand and
predict large
-
scale ocean dynamics

and

ocean

atmos
phere interactions
.
Marine meteorological
variables include those needed to characterize fluxes of momentum, heat and fresh water across
the air

sea interface, namely, surface winds, SST, air temperature, relative humidity, downward
short and long
-
wave rad
iation, barometric pressure and precipitation. Physical oceanographic
variables include upper
-
ocean temperature, salinity and horizontal currents. From these basic
variables, derived quantities, such as latent and sensible heat, net surface radiation, pene
trative
shortwave radiation, mixed
-
layer depth, ocean density, and dynamic height (the baroclinic
component of sea level) can be computed. The array design focuses on these marine
meteorological and physical oceanographic variables, though not all moorings

will measure all
variables. The moorings can also support sensors to measure CO
2

concentrations in air and sea

1
9

water, nutrients, bio
-
optical properties and ocean acoustics (
International Clivar Project Office,
2006
).


The
Global Tropical Moored Buoy Arra
y (GTMBA) is a multi
-
national effort to provide
meteorological and ocean observational data in real
-
time for climate research and forecasting
(McPhaden
et

al
.
,

2009
a
).

The buoys are used to collect oceanographic and meteorological data
for monitoring forec
asting, and climate research, particularly
for
ENSO studies.

The array
consists of
the
Tropical Atmosphere Ocean/Triangle Trans
-
Ocean Buoy Network
(TAO/TRITON) in the Pacific, the Prediction and Research Moored Array in the Tropical
Atlantic (PIRATA), and
the Research Moored Array for African
-
Asian
-
Australian Monsoon
Analysis and Prediction (RAMA) in the Indian Ocean. These observing system
s

were designed
and implemented within the framework of
GOOS and GCOS.

The primary objectives are to
study intraseasona
l to decadal time scales including ENSO and the Pacific Decadal Oscillation
(PDO) in the Pacific, the meridional gradient mode and equatorial warm events in the Atlantic,
the IOD and the Madden
-
Julian Oscillation (MJO) in the Indian Ocean, the mean seasona
l cycle,
including the Asian, African, Australian, and American monsoons and trends in all three basins
that may be related to global warming. However, these observations will complement other in
-
situ and satellite observational components of Global observ
ing systems.


The GTMBA is built primarily around
the
Autonomous Temperature Line Acquisition System
(ATLAS) moorings
of
NOAA’s Pacific Marine Environmental Laboratory (PMEL) and
TRITON moorings
of
Japan Agency for Marine
-
Earth Sci
ence and Technology (JAMS
TEC).
The Schematic diagram of
the
ATLAS mooring
s

with
the
location
s

of different sensors fitted on
the buoy
s

and on the mooring
s

is
available in PMEL website.
These moorings have special
attributes that make them a valuable technology

for tropical climate

studies.
In particular, 1) they
can be instrumented to measure both upper ocean and surface meteorological variables involved
in ocean
-
atmosphere interactions; 2) they provide time series measurements at fine temporal
resolution (minutes to hours) to reso
lve high frequency oceanic and atmospheric fluctuations that
would otherwise be aliased into the lower frequency climate signals of primary interest; 3) they
can be deployed and maintained on a fixed grid of stations
,

so that measurements do not
distort
th
e

variability in time and space.
The data from surface moorings are transmitted to shore via

20

ARGOS satellite in real
-
time, which
ensures (i) use of thes
e data for
operational weather, Ocean,
and climate forecasting and (ii) retrieval of data even if a moo
ring is lost.

The data are posted
daily and made freely available on the NOAA/Pacific Marine Environmental Laboratory
GTMBA web site (
http://www.pmel.noaa.gov/
tao/global/

global.html) as well as several web
sites
maintained by partner institutions around the world. Service Argos inserts the data on the
GTS several times a day
.
Details about
different
types of moorings used in the GTMBA,
including subsurface ADCP and deep ocean moorings, can be found in
McPhaden
et

al
.
, (2009

a
).

Mooring sensor specifications (accuracy, resolution, range), sensor calibration procedures,
and data quality control for both real
-
time and delayed mode data streams are available from
websites maintained by PMEL
and JAMSTEC website.
The p
resent status of GTMBA array

in
the Global ocean

is shown in Fig.
5
.


TAO/TRITON data have been used in over 600 refereed journal publications since its inception
in 1985. TAO/TRITON has been the dominant source of upper ocean temperature data near the
e
quator over the past 25 years. The data show that depth average temperature in the upper 300
m, an index for upper ocean heat content, leads Niño3.4 SST
(area
-
average SST anomalies
between 5ºN
-
5ºS and 170º
-
120º W)
typically by 1
-
3 seasons. A build up of

heat content at the
end of this record, followed by rising Niño3.4 SSTs, indicates development of the current 2009
El Niño event. This relationship between upper ocean heat content and SST not only validates
recharge oscillator theory, but also highlights

the role of heat content as the primary source of
predictability for ENSO. The simple relationship
has
motivated the inclusion of upper ocean heat
content as a predictor in some statistical ENSO forecast models (e.g., Clarke and van Gorder,
2003; McPhaden

et al
.
, 2006), analogous to the assimilation of upper ocean temperature in
dynamical ENSO forecast models (e.g., Latif
et al
.
, 1998). PIRATA data have been very
influential in identifying the causes of the observed SST variations in the tropical north Atl
antic
over the past 10 years (McPhaden, 2008). Year
-
to
-
year swings in tropical north Atlantic SST
appear to be principally related to wind
-
evaporation
-
SST feedbacks (Chang
et al
.
, 2001) with
contributions from shortwave radiation and horizontal advection.



RAMA, even in the initial stages of development, is providing valuable data for describing and
understanding variability in the Indian Ocean. For example, a pronounced semiannual cycle in

21

upper
-
ocean temperature, salinity, and zonal velocity is evident
in the first three years of data
from near
-
equatorial moorings at 90°E (Hase
et al
., 2008). The semi
-
annual velocity variations
are referred to as Wyrtki Jets and their zonal mass transports are largely governed by wind
-
forced linear dynamics (Nagura and M
cPhaden, 2008). They are also strongly modulated on 30
-
50 day intraseasonal time scales related to the MJO (Masumoto
et al
.
, 2005). Variations in
meridional velocity on the equator in contrast are dominated by higher frequency 10
-
20 day
period oscillations
, which are evident not only in the upper 400 m but also at depths greater than
2000 m (Murty
et al
., 2006; Ogata
et al
., 2008). Sengupta
et al
.
,

(2004) identified these
oscillations as wind forced mixed Rossby
-
gravity waves.
RAMA data indicate that subsur
face
temperature variations lead those at the surface by a season near the equator in eastern basin,
suggesting that upper ocean thermal structure may be a source of predictability for the IOD as in
the Pacific for ENSO (Horii
et al
.
, 2008).
Moored buoy d
ata are routinely used in ocean state
estimation, operational ocean analyses, operational atmospheric analyses and reanalyses.
T
hese
data have also
been
used extensively for model validation, and for satellite validation of surface
winds, SST, rainfall,

a
nd shortwave radiation
.


Further, in order to build and maintain a multidisciplinary global network for a broad range of
research and operational applications, the new program “OceanSITES” is evolving (Send,
et al
.
,
2009). The OceanSITES program is the glo
bal network of open
-
ocean sustained time
-
series
measurements, called ocean reference stations, being implemented by an international
partnership of researchers. OceanSITES provides fixed
-
point timeseries of various physical,
biogeochemical, and atmospheric

variables at different locations around the globe, from the
atmosphere and sea surface to the seafloor. OceanSITES moorings are an integral part of the
Global Ocean Observing System. They complement satellite and other in
-
situ data by adding the
dimension
s of time and depth. All OceanSITES data are publicly available. More information
about the project is available at http://www.oceansites.org.


2.
8

Argo

Profiling floats

The Argo “
o
ceanographic radiosonde” is a revolutionary concept that enhances the real
time
capability for the measurement of temperature and salinity through the upper 2000 m

in the ice

22

free global Ocean
.
The exclusion of the high latitudes was due to the inability of early

floats
to
sample under sea
-
ice
.
However, t
echnological advances in
float design in recent years now give
us this capability.

A
dvancements have come through re
-
design of hardware (i.e.

armoured

ice
floats
with
ice
-
hardened antennae), software (ice
-
avoidance algorithms and open
-
water test) and
c
ommunications (Iridium), all
owing the transmission of stored winter profiles.

Following the
geostrophic principles,
along with reference level velocities of the ocean
,
i
t contributes to the
global description of the variability of the upper ocean thermo
-
haline structure and circulat
ion on
seasonal and inter
-
annual time scales. Under a unique, internationally coordinated effort,
it has
been established
as
a global array of about 3000 floats at a spatial resolution of 3

x

3

deg g
rids
.

The data from these floats have helped to study th
e state of the upper ocean and the patterns of
ocean climate variability, including heat and freshwater storage and transport

(
Howard
Freeland
et al
., 2009).
The data are collected by
Argo
floats that spend most of their working life drifting
with the curr
ents at depth (they are
stabilized

at a constant level by being less compressible than
sea water)

of 1000 or 2000 m
. At typically 10 day intervals
,

the floats pump fluid into an
external bladder and rise to the surface (taking about 6 hrs) and
measure

a p
rofile of temperature
and salinity. On surfacing the data are downloaded to the satellites
(ARGOS or Iridium)

which
also obtains a series of float positions. When this task is completed the bladder deflates, the float
thus returns to its original densit
y and returns to depth to drift until the (usually 10 day) cycle is
repeated.

Data from Argo floats are available to users through two streams


real time (with only
gross errors corrected or flagged) and delayed
-
mode (where corrections to salinity values
have
been estimated by experts familiar with the particular geographical environment). At present the
delayed mode data delivery system has yet to be fully implemented. The real time data are
placed on the
GTS
that delivers (mostly meteorological) data t
o operational
centers throughout
world
. They are also available through two linked Argo Global Data Centres (GDACs) in Brest,
France (Coriolis) and Monterey, California (US GODAE server).

The global distribution of
floats reporting on the Argo system as o
n Nov 10, 2009 is shown in Fig. 6.


Argo floats

bridge the
complementary nature of the direct and remote observing systems, filling
the large gaps that exist in the global sampling network, and provid
ing
essential information for
sub surface
ocean state es
timation.
The
combination

of Argo

and satellite altimetry has enabled a

23

new generation of applications. Global maps of sea level, on time scales of weeks to several
years,
will be
interpreted with full knowledge of the upper ocean stratification. Global O
cean
and climate models can

be

initialized, tested and constrained with a level of information hitherto
not available. The drift estimates from such an array would in addition provide useful estimates
of deep pressure fields (reference level).


Altimeter
s, together with the sea level gauge network, provide accurate measurements of time
-
varying sea surface height (SSH) globally every 10 days.
On seasonal and longer time
-
scales,
SSH is dominated by changes in subsurface density.
The cause of

mean
sea level

change is
mainly due to change in volume and shape of the ocean basins

at

comparatively long time scales.
The change in volume is caused by the changes in sea water density (steric) and mass (eustatic).
The change in temperature (thermosteric) and salinit
y (halosteric) of the water column can
change sea water density, whereas melting of glaciers in land and Artic and Greenland ice will
change the mass of the water in the Ocean. The shape of the ocean basin changes due to vertical
land movement, which is as
sociated with local tectonic activity and post glacial rebound of land.
The contribution of

the

steric and eusatic for the total sea level rise can be quantified using Argo
profiling floats and GRACE
respectively
and which can indirectly compared to altime
ter sea
level data.
On global scales, Argo and Jason, together with satellite gravity measurements,
partition global sea level rise into its steric and mass
-
related components (Willis
et al
., 2008,
Cazenave
et al
., 2009, Leuliette and Miller, 2009, Wunsch
et al
., 2007).


Applications of Argo data are numerous and varied, including initialization of ENSO forecast
models, initialization of short
-
range ocean forecasts, routine production of high
-
quality global
ocean analyses, and studies of predictability on
inter
-
annual and decadal time scales.
A
substantial improvement in seasonal forecast skill due to Argo profile data has been
demonstrated (Balmaseda and Anderson, 2009), even during the
period prior

to full deployment
of the Argo array. The combination of
Argo
(provides spatial coverage)
and moorings
(
provides
the high temporal resolution needed for equatorial wave propagation and intra
-
seasonal
variability
and also
for observing tropical variability at greater depth (Matthews
et al
., 2007), and
beyond the
equatorial band and in all oceans (Cai
et al
., 2009). Data from Argo and RAMA were

24

used to illustrate air
-
sea interaction contributing to the growth of the devastating 2008 tropical
cyclone Nargis

(McPhaden
et al
., 2009

c
)
.


Heat and
fresh
water are fundam
ental elements of climate, and climate variability can be
quantified by tracking heat and
fresh
water as th
ey are transported and stored
, and exchanged
between, the atmosphere, oceans, land, and cryosphere.
The temperature and salinity profile
measurements

over
the
global ocean
provides the

estimates of both the storage and large
-
scale
transport of heat and freshwater

(
Howard
Freeland
et al
.
, 2009 and reference therein).

Although
Argo is
able to
provide information on the
ocean’s

role in the planetary heat
and water budgets
,

the important contributions of boundary currents (Send
et al
., 2009) in ocean heat transport and
of the abyssal oceans in heat storage are not yet adequately observed

since the
boundary currents,
fronts, and eddies
require
finer resoluti
on

in the observing sampling rages
.
The most direct effect
from

the

ocean comes from the surface effect i.e sea surface temperature
as well as

sea level
variability.
Satellites provide global views of sea surface temperature and, in future, sea surface
sal
inity. These data
require
in
-
situ

measurements

for calibration purposes, and for their
interpretation. Argo can help satisfy both of these requirements.
For example,
Uday Bhaskar
et
al
.,

(2009)
,

using satellite and in
-
situ data,
have shown that Argo near s
urface temperature

(5 or
10 m)

can be used as SST
in

the

Indian Ocean.

Argo’s observation of surface layer structure
globally
, contributes

to studies of atmosphere
-
ocean interactions
. (
Howard
F
r
eeland
et al
.
, 2009
and reference
s

therein
).


Ocean salinity
is an important component (indicator) of
the
“global water cycle” variability. It
provides information on the exchange of freshwater with the atmosphere (e.g., evaporation,
precipitation) and with the terrestrial and cryo
s
pheric components of the global cl
imate system,
and on storage within the ocean. Ocean salinity is a fundamental ocean state variable and a tracer
of ocean circulation

an important dynamical ocean process that governs the uptake and
redistribution of ocean heat and carbon,
which are
critic
al elements of the global climate system.
Thus to understand and predict the global water cycle in the context of global climate change it
be only be fully realized with the understanding of the marine branch of the hydrological cycle
.

Also ocean salinit
y changes have a direct impact on the exchange of CO
2

between ocean and
atmosphere and may affect marine species and ecosystems.


25


Current knowledge of ocean salinity variability is hampered by
a
lack of
enough
long
-
term
salinity records. Available observat
ions indicate that remarkable changes of ocean salinity are
underway in some regions. Unfortunately, it is unclear if these changes are attributable to natural
variations, what processes may be involved, how they may or may not be consistent with
changes i
n other components (e.g., precipitation) of the global water cycle, how long such
changes have been underway, or how widespread they might be. The Argo float observation

network is a critical component of a global salinity observing system
.



2.9 HF Radar

Real
-
time surface current information is a valuable supplement to understanding coastal air
-
sea
interaction

and dynamical processes at the coastal scales
. Coastal surface current information
may be correlated to winds and tidal currents among other physica
l phenomena. High
-
frequency
(HF) radars have been used for measuring surface current fields and ocean
-
wave spectra. The
physics behind HF radar is based on backscattering from a moving rough sea surface.
The
Radar
t
ransmits
electromagnetic waves

of 6 MHz t
o 30 MHz

(50 m to 10 m wavelength), which travel
along the sea surface beyond the horizon by
ground wave propagation

and
are
scattered
back

from ocean waves of half the electromagnetic wavelength (
Bragg scattering
). The scattered
signals are measures of th
e Doppler spectrum caused by moving waves and speed of the surface
currents carrying the ocean waves.
Guided propagation along the conductive sea surface (ground
wave) allows measurements beyond the horizon.
It can also be inferred
O
cean wave height and
t
he wave directional spectrum using second
-
order sea echos of the Doppler spectrum. T
he
Doppler shift of the backscattered signal is used for measuring the radial current speed relative to
the radar site. If the two radar sites measure the radial velocity
of a patch of water from two
different angl
e
s, it is possible to calculate the two horizontal components of the surface velocity.
The surface current measured is a horizontal mean over several km in both range and azimuth,
over a
pproximately

the upper 0.5
-

1.0 m of the ocean (penetration depth of scattering ocean
waves), and over some 10 minutes measuring time. These radar sites provide coastal
-
ocean
surface current and wave information offshore out to 300 km. More detailed descriptions of the
theory of
HF

radar

can be found in numerous articles (e.g., Gurgel
et al
.
,

1999b; Barrick
et al
.
,

1985).


26


As part of
the
integrated ocean observing system (IOOS),
the
US has installed
a
number of

HF
r
adar
s on the
west and east coast
s

of
US. Prototype real
-
time data a
rchitecture, initially
developed through funding from the National Science Foundation (NSF), is now being
integrated by the Coastal Observing Research and Development Center (CORDC) at the Scripps
Institution of Oceanography with existing HF radar data ne
tworks through a joint development
program administered and managed by the National Data Buoy Center (NDBC) and the National
Ocean Service (NOS), with oversight provided by the National Oceanic and Atmospheric
Administration’s (NOAA) IOOS program office (T
erril
et al
.
, 2002). An excellent online
reference containing an introduction to the principles of HF Radar can be found on the Rutgers
University Coastal Ocean Observation Lab (RUCOOL
,
http://marine.rutgers.edu/
cool
)
.

T
he
coastal radar locations o
n the

east
/
west
coast of
the
US and daily average values of surface
currents (6 km) derived from HF Radar along the coast of
the
US
are

available
at
http://cor
dc.ucsd.edu/projects/mapping/maps/
.



The validation of both wave with moorings and current observations with surface drifters are
explained in detail by Kohut (2000). Surface current observations using HF Radar and its
assimilation into the NewYork Harb
our observing and prediction system has been reported by
Gopalakrishnan (2008). There are many coastal ocean radars
that
have been installed all around
the coastal stations. The data provided by the coastal ocean Radar is already useful for many
operation
al applications and Research use (http://www.codar.com/bib_05
-
present.htm).


2.
10

Gliders

Gliders are small autonomous underwater vehicles which were developed to carry out in
-
situ
observations of the upper 1km of the ocean
.
They enhance the capabilities o
f
profiling floats

by
providing some level of maneuverability and hence position control. They perform saw
-
tooth
trajectories from the surface to depths of 1000

m, along reprogrammable r
outes (using two
-
way
satellite link). There is around ~2
-
6 km between surfacing when diving to 1km depth. They
achieve vertical speeds of 10
-
20

cm/s and forward speeds of 20
-
40 cm/s and can be operated for
a few months before they have to be recovered (Dav
is
et al
., 2002). They can record temperature,
salinity, pressure data and depending on the model some biogeochemical data, such as dissolved

27

oxyge
n and fluorescence/optical backscattering at various angles/wavelengths (Chl
-
a, CDOM,
phycoerythrin, turbidit
y, etc,.). They can also be equipped with acoustic modems and
hydrophones for underwater positioning and underwater data telemetry.


Gliders can “fly” underwater along slightly inclined paths without propeller. A change in volume
(generated by filling an
external oil bladder) creates positive and negative buoyancy. Because of
the fixed wings, the buoyancy force results in forward velocity as well as vertical motion. So
gliders move on a sawtooth pattern, gliding downward when denser than surrounding water
and
upward when buoyant. Pitch and roll can be controlled by modifying the internal mass
distribution and gliders automatically align the positions of the center of buoyancy and the center
of gravity to achieve desired angle of ascent/descent. Either a rud
der or a roll control is used for
navigation through lists of waypoints. The high efficiency of the propulsion system enables
gliders to be operated for several months during which they may cover thousands of kilometers.


Davis
et al
.,

(2008) ha
ve

operated

g
lider
s

over many years in the eastern pacific
to perform

repeat sections
.

Similar long sections along the coasts of the USA in the Pacific, and in the
Atlantic
(Castelao
et al
.
,

2008; Glenn
et al
.
, 2008; Perry
et al
., 2008
) demonstrated the capacity
of

gliders to carry out, over years,
measurements of the local vertical structure of the ocean over
0
-
200m or 0
-
1000m from the near
-
shore environment (10
-
100m depth) to the open sea (hundreds
of km offshore). Other important
aspects of

gliders are (1) the lo
ngest glider section ever done
with one set of batteries is 6000km long
(Eriksen and Rhines, 2008)
and (2) crossing very high
currents is possible (such as the Gulf Stream,
Nevala, 2005
).
The
Australian National facility of
ocean Gliders (ANFOG) under IMOS

uses gliders
to observe

the boundary currents
and
shelf
processes around Australia.


Glider technology is advancing quickly, and will be ideal for monitoring water masses and
currents in a variety of oceanic regimes.
In regions of
divergence zones and the

boundary
currents near the continental slope or steep topographic features, glider
s contribute immensely to
measuring sub surface parameters. This will also facilitate
in

understand
ing

meso and sub
-
meso
scale processes. Presently, the assimilation of gli
der data is already operational for T
-
S, in

28

regional
and global models
(
Pierre
Testor, et
.
al, 2009).

The real time data are being archived at
the
Coriolis Data Center,
Brest,
France.


3.
Basin scale Observing system

-

Ind
OOS


Of the three major oceans


Pa
cific, Atlantic, and Indian


the Indian Ocean has is the only one
that is not open to the northern subtropical regions. This is a consequence of the presence of the
Asian landmass restricting the Indian Ocean to south of about 25°N

and hence it cannot
tr
ansport heat gained in the tropics to the higher northern latitudes, as the Pacific and Atlantic
oceans
do, mainly via their western boundary currents. Further
more
,
the
Indian Ocean is the only
ocean with a low
-
latitude opening in its eastern boundary and
gains additional heat from the
tropical Pacific via the Indonesian Throughflow.
The unique geography has important
implications for the oceanic circulation physics, and consequently for climate and the
biogeochemistry of the ocean, giving the Indian Ocean
many unique features. Heat is carried
southward along the western coast of Australia toward the southern subtropics. The Indian Ocean
consequently has a unique system of three
-
dimensional currents and interactions with the
atmosphere that redistribute heat

to keep the ocean approximately in a long
-
term thermal
equilibrium (
International Clivar Project Office, 2006
).
Further, t
he strong influence of monsoon
systems generates distinct seasonal variations in the upper ocean. Also, previous attempt
s

to
measure

and simulate the ocean variability reveals rich spectrum of variability spanning from
intraseasonal to interannual, decadal, and much longer time
-
scale phenomena. Combination and
interaction among these phenomena cause significant climate variability over

and around the
Indian Ocean. Despite
such an
important role of the Indian Ocean
such as
monsoons, climate
variability
a
nd its impact on global climate change through atmospheric and oceanic
teleconnections, a long
-
term, sustained observing system in the I
ndian Ocean had not been
started
. This had left
the Indian Ocean as the least observed ocean among the three major basins.
Recognizing this observation
-
gap, an enthusiastic spirit emerged after the OceanObs99 meeting,
result
ing

in the development of a plan

for the Indian Ocean Observing System (IndOOS) under
the
coordination of the CLIVAR/GOOS Indian Ocean Panel

(Meyers and Boscolo, 2006)
.
The
schematic diagram of IndOOS and the regional Observing system is shown in Fig. 7.



29

The outstanding research issues
that need to be addressed with observations to advance
the
understanding of the role of the Indian Ocean in the climate system and its predictability are (i)
Seasonal monsoon variability and the Indian Ocean, (ii) Intraseasonal variability, (iii) Indian
Oc
ean zonal dipole mode and El Niño

Southern Oscillation, (iv) Decadal variation and warming
trends in the upper Indian Ocean, and (v) Southern Indian Ocean and climate variability, (vi)
Circulation and the Indian Ocean heat budget (Indonesian Throughflow, s
hallow and deep
overturning cells), (vii) Biogeochemical cycling in the Indian Ocean and (viii) Operational
oceanography.

The status of each element of IndOOS is briefed below.


3.1
Moorings

The basin
-
scale mooring array is essential for understanding and
identifying their limits of
predictability of the role of the ocean in the Monsoon Intraseasonal oscillation (MISO) and
Madden
-
Julian Oscillation (MJO), which are long lasting weather patterns that evolve in a
systematic way for periods of four to eight we
eks. The intense, long
-
lasting weather conditions
associated with MISO and MJO interact strongly with the temperature and salinity structure of
the ocean mixed layer, but the physics is not yet understood nor is it fully built into coupled
models. The rol
e of surface currents in the evolution of intraseasonal variation is not known. The
air

sea heat and freshwater fluxes are poorly known. The array will provide vital information on
these processes. It is also needed to understand the mixed
-
layer dynamics a
nd the role of currents
in interannual variation
s
, such as

the

IOD. Operational ocean
-
state estimation, such as the
production of daily maps of currents and thermal structure for marine industry and defence, is
not possible without the array. While this re
port is primarily concerned with oceanographic
measurements, the meteorological measurements (particularly at moorings) will be extremely
valuable to data assimilation issues concerned with weather forecasting and reanalysis efforts.


The sub surface moor
ing array, called
the

Research moored Array for African
-
Asian
-
Australian
Monsoon Analysis and prediction (RAMA)

( McPhaden
et al
., 2009 b) consists of a total of 46
moorings, of which 38 are ATLAS/TRITON
-
type surface moorings.
Seven of these
surface
moorin
gs are selected as surface flux reference sites, with enhanced flux measurements. The
surface mooring system can measure temperature and salinity profiles from the surface down to
500 m depth as well as the surface meteorological variables, and the observe
d data is transmitted

30

in real
-
time via Argos satellites. In addition to these surface buoys, there are five subsurface
ADCP moorings along the equator to observe current profiles in the upper equatorial ocean, and
three deep current
-
meter moorings with ADC
Ps in the central and eastern equatorial regions. The
RAMA array design was evaluated and supported by observing system simulation experiments
(Oke and Schiller, 2007; Vecchi and Harrison, 2007). The array has
been implemented

rapidly
in recent years, lar
gely through bi
-
national activities involving Japan, India, USA, Indonesia,
China, France, Holland and South Africa.


Early observations of this mooring provide invaluable data set for analyses on the
Indian Ocean

variability

(Masumoto
et.al
., 2009

and re
ference
s

therein
)
. For example, a long
-
term current
observation at 90E on the equator reveals that there is significantly large amplitude intraseasonal
variability both in the zonal and meridional components as well as the well
-
known semiannual
and annual
variations
. Also, RAMA mooring data used to
capture subsurface evolution of the
three consecutive Indian Ocean Dipole event from 2006 to 2008, with clear negative temperature
anomaly at the thermocline depth that appeared a few months before the surface s
ignatures of the
IOD events
. Mooring data were also used to observe
the oceanic response to cyclone “Nargis”,
which made landfall in Myanmar on 2 May 2008. Intense ocean mixing and significant turbulent
heat loss from the ocean surface (~600 W/m
2
) occurre
d as Nargis passed near RAMA buoy at

15°N, 90°E in the Bay of Bengal (McPhaden
et al
.
, 2009
c
).

Surface moorings from the RAMA
array
also
allowed process studies of the strong upper ocean response to the MJO in the
Seychells
-
Chagos Thermocline Ridge region
(Vialard
et al
.
,

2008)
.




3.2
Argo profiling floats

Argo floats are another revolutionary change in in
-
situ ocean observing in the Indian Ocean. The
build
-
up began in 2003 as part of the global description of the variability of the upper ocean
thermohali
ne structure and circulation on seasonal and inter
-
annual time scales. Data from these
floats, together with the satellite based
-

and other in
-
situ observations, would enhance the
understanding of the ocean circulation pattern and its influence on the glob
al climate variability
and would contribute to improve prediction skills of seasonal climate variability.
The Indian
Ocean (north of 40°S) requires 450 floats to meet the Argo design of one float per 3

x

3 deg

grid
.

Around 441 floats are active as on Oct 3
1, 2009. Still there are some gaps and other places where

31

more than required floats are present.

The Argo program's unprecedented spatial and temporal
coverage of density and geostrophic current is opening new perspectives on circulation
-
research.
The new

observations combined with a hierarchy of models are likely to
address
many
unanswered questions.

Argo observations in the Indian Ocean are creating many new insights by many different authors
studying
many

aspects of the Indian Ocean. Thus Argo enables
a new understanding of the upper
ocean variability of Arabian sea, such as summer cooling during contrasting monsoons
,
temporal
variability of the core
-
depth of Arabian Sea High Salinity Water mass (ASHSW), buoyancy flux
variations and their role in air se
a interaction, identification of the low
-
salinity plume off the
Gulf of Khambhat, India, during post
-
monsoon period, mixed layer variability of western
Arabian Sea, seasonal variability of the observed barrier layer
, t
he importance of upper ocean
temperatu
re and salinity during cyclone
s
,

to reveal a pronounced westward propagation of
subsurface warming in the southern tropical Indian Ocean associated with Rossby waves on the
sloping thermocline
,
intense cooling of the sea surface at intraseasonal time scale
s in the
southern tropical Indian Ocean during austral summer
, etc,.. Also these data are used to study the
the
i
mpact of assimilation in simulating temperature and salinity in the Indian Ocean
(Masumoto
et al
.
, 2009 and reference
s

therein).


3.3
SOOP/XBT
lines

Several SOOP XBT lines obtain frequently repeated and high
-
density section data. The
frequently repeated lines in the Indian Ocean are narrow shipping routes allowing nearly exact
repeat sections. At least 18 sections per year are recommended in orde
r to avoid aliasing the
strong intraseasonal variability in this region. The CLIVAR/GOOS Indian Ocean Panel reviewed
XBT sampling in the Indian Ocean and prioritized the lines according to the oceanographic
features that they monitor (
International CLIVAR
Project Office, 2006). The highest priority was
on lines IX1 and IX8. The IOP recommended weekly sampling on IX1 because of the
importance of throughflow in the climate system. IX8 monitors flow into the western boundary
region, as well as the Seychelles
-
C
hagos Thermocline Ridge, a region of intense ocean
-
atmosphere interaction at inter
-
annual time scales . IX8 has proven to be logistically difficult
to
implement,
so an alternate line may be needed.



32

More than 50 papers have been published based wholly or
in part on the frequently repeated
XBT lines in the Indian Ocean. The research results include

to understand the
seasonal,
interannual and decadal variation of volume transport of major open ocean currents
,
c
haracterization of seasonal and interannual var
iation of thermal structure and its relationship to
climate and weather
(
e.g.

the IOD
, tropical cyclones
)

,
s
urface layer heat budget to identify the
relationship between sea surface temperature, depth of the thermocline and ocean circulation at
interannua
l to decadal timescales
,
Rossby and Kelvin wave propagation

and
v
alidation of
variation of thermal structure and currents in models

(Masumoto
et al
.
, 2009 and reference
s

therein)
.


3.4
Drifting Buoys

The surface drifting buoys
in
the
Indian Ocean to mee
t the design of one buoy
in every 5
-
degree
box
.

As of Nov 2009, 62 drifters are active in the Indain Ocean north of 40
º
S and only 10
drifters are in
the
North Indian Ocean.
A problem in the Indian Ocean is that the strong Asian
summer monsoon winds drive

drifters out of the North Indian Ocean.
Considering that the
drifters are following the flow,
the possible option for keeping the required number of drifters are
(i) the criteria should be readdressed, (or) (ii) a different measuring platform need to be
i
mplemented in these regions or (iii) a more

frequent seeding program is needed to maintain the
5
-
degree sampling. The design sampling density is to support calibration of satellite SST

and to
build surface current climatology
. To our knowledge, the sampli
ng density required to map
surface currents at say monthly time scale has not been determined, but should be to validate
surface currents in models and reanalyses.


3.5
Data Management

The data portal for the Indian Ocean data collected in support of IndOO
S is available at
http://www.incois.gov.in/Incois/iogoos/home_indoos.jsp
, which
relies on a distributed network
of data archives. The main idea is to provide a one
-
stop shop for Indian

Ocean
-
related data and
data products. The core of the system is a web portal maintained at INCOIS, providing direct
binary access to the data via OPeNDAP and ftp protocols. Web
-
based browsing and data
discovery are handled through custom
-
designed web tool
s and currently available on servers
such as the Live Access Server (LAS). The distributed data archives are maintained by the

33

individual groups at their institutes and made available to the community via the web portal. The
portal contains data from basin
-
scale
observations using mooring arrays, Argo profiling floats,
expendable bathythermographs (XBT), surface
-
drifters and tide gauges, as well as the data from
regional/coastal observation arrays (ROOS) to observe boundary currents off Africa (WBC), in
the

Arabian Sea (ASEA) and Bay of Bengal (BOB), the Indonesian throughflow (ITF), off
Australia (EBC) and deep equatorial currents. Satellite derived gridded data sets such as sea
surface temperature (TMI), sea surface winds (QuikSCAT) and sea surface height
anomaly
(merged altimeter products) are also available. The agencies contributing to the IndOOS are
committed to follow the CLIVAR data policy (
http://www.clivar.org/data/data_policy.htm
).


IndOOS provides the backbone for a number of planned process stud
ies associated with
international programs such as Vasco
-
Cirene, MISMO (Mirai Indian Ocean Cruise for the Study
of the MJO
-
Convection Onset), TRIO (Thermocline Ridge of the Indian Ocean), CINDY2011
(Cooperative Indian Ocean experiment on intraseasonal vari
ability in the Year 2011), DYNAMO
(Dynamics of the Madden
-
Julian Oscillation), and the Year of Tropical Convection.
IndOOS
also supports the various regional observing systems around the Indian Ocean, which further link
IndOOS.
Incorporation of observation
s for bio
-
geochemical parameters will be a necessary
future step forward to enhance interdisciplinary research in the Indian Ocean sector. A new
satellite that measures surface salinity distribution would be another significant challenge in the
Indian Ocea
n, where the large salinity contrast between the Arabian Sea and the Bay of Bengal
play

an

important role in the climate system of the surrounding regions.


4.
Summary

Analysis and interpretation of the ocean observational data require the knowledge of d
ifferent
time and space scales over which each processes characterize
s: from the

turbulent eddies with
durations of few seconds and spatial scales of centimeters (high frequency) to wind
-
forced and
thermodynamically driver ocean currents with time scales o
f days to centuries and spatial scales
of tens to thousands of kilometers (low frequency).

The e
xchange of momentum, heat, salt and
other tracers within the ocean and across the air
-
sea interface occurs in these space and time
scales.
The
observing system

should resolve timescales of seconds to decade
s

by measuring
continuously without much gap and
the
spatial scales
as close as possible.

These observations

34

have to withstand and provide good quality of data during disturbed or extreme weather events.
Also

these observational data needs to be available to operational agencies in real
-
time.


Research advances and paradigm shifts in oceanography have often been stimulated by
observations of various processes; in particular, theories and models have typically

been
developed to explain, quantify, incorporate, or parameterize these processes using balance
equations. Examples of this chronology include Ekman transport, western intensification of
boundary currents, and seasonal phytoplankton blooms. Limitations of

oceanographic data
persist in terms of raw numbers and diversity of variables. This is not surprising considering the
spatial scale of the oceanic setting and the time scales of interest, both of which can span over 10
orders of magnitude

and

the complexi
ty of the biology, chemistry, and physics of the oceans.
Progress in addressing ocean sampling deficiencies using interdisciplinary, multiplatform
sampling is best
reviewed by D
ickey (2003)
. Ocean models have become increasingly useful as
new processes hav
e been incorporated or parameterized in formulations, numerical techniques
have been improved, and more powerful computing capabilities have allowed increa
sing

spatial
and temporal resolution and range as well as greater numbers of variables and balance eq
uations.
Interestingly, as more data have been collected, analyzed, and interpreted, and as computer
model simulations have become more realistic
.

observationalists and modelers have become
more cognizant of and dependent upon each other for making scienti
fic advances. The
culmination of this cultural change is epitomized by two methodologies: inverse methods and
data assimilation (Dickey, 2003).

Though
platforms represent

the backbone of the observational
component of any data assimilation system
, integrat
ed m
ultiplatform approaches have been
adopted by several major oceanographic process and time series studies in order to take
advantage of the specific sampling capabilities of individual platforms, which generally can
carry a variety of interdisciplinary
sensors and systems and often have telemetry capabilities.
The
strengths and weaknesses of
each

platform

are shown in
T
able

1.


In order to maximize value of the observing system as a whole, it is critical for a set of core
variables to be selected includi
ng some that are common to repeat hydrography and autonomous
instruments
-

moorings, floats, and gliders. If deep ocean floats are developed and deployed in
Argo, then validation and correction, requiring repeat hydrography, will be needed for those

35

instru
ments as well as for the upper ocean ones. In the event that a deep float array is not
deployed, then the observation of deep ocean changes in heat, freshwater, and steric sea level
would rest entirely with the repeat hydrography program. Because there are

gaps in the planned
global sampling, of scale 5,000 km, errors in estimation of global integrals would be substantial.
Studies are needed to assess the likely errors in global ocean heat content with and without a
deep float array (
Dean
Roemmich,
et al
.,

2009 & Hood et. al 2009).


The individual networks of the present Sustained Ocean Observing System for Climate including
tropical moorings, XBTs, surface drifters, ship
-
based meteorology, tide gages, Argo floats,
repeat hydrography, and satellite observati
ons have developed largely independently of one
another. Progress will now come from integration across the networks since the next big
observational challenges
-

including boundary currents, ice zones, the deep ocean, biological
impacts of climate, and th
e global cycles of heat, freshwater, and carbon
-

demand multiplatform
approaches and because exploiting the value of ocean observations is intrinsically an activity of
integration and synthesis (
Dean
Roemmich
,
et al
.,

2009).

Also, discussed the synergies
of the
observing system on which improved integration will be built, and key infrastructures that will
underpin an integrated global observing system, potential developments and improvements in the
different in
-
situ platform networks.
The time is now appro
priate to consider integrating these
observational programs, with a view to facilitating the effective direct application of the
knowledge and predictive capacities that are and will continue to be gained from these studies
(Masumoto et. al, 2009).

The lar
gest increments to be gained over the present global observing
system will come from expanding the sampling domains of autonomous platforms, from addition
of multi
-
disciplinary measurements, and from integrating developments in data quality,
coverage, and
delivery.


Acknowledgements


Most of the work described above is based on
the
Community White Paper, OceanObs’ 09.
Highest appreciation is placed on record for the excellent compilation by several authors and
organization for their Community White Paper
,

it would have been difficult without these White
papers.

The encouragement and the facilities provided by the Director, INCOIS
is

36

acknowledged.

Also acknowledged Wee Cheah, University of Tasmania
,

Sabastiaan Swart
,
University of Cape T
own
and unknown re
viewer
for critically
going through the manuscript to
improve it.




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43

Figure Legends


Fig. 1. Status of reporting of the sea level gauges in the GLOSS Core Network in 2009. Near
real
-
time stations (blue) pro
vide data typically within 1 hour of collection; Fast delivery (green)
within one month. Delayed mode low frequency data within 5 years (yellow) or greater (orange)
include monthly averages provided to the Permanent Service for Mean Sea Level (PSMSL).
(Sou
rce: Merrifield
et al
.
, 2009).

Fig. 2. (top) XBT network containing OceanObs99 recommendations and

(bottom)

proposed
transects in OceanObs09. XBT observations transmitted in (red) real and (blue) delayed
-
time in
2008. (Source: Goni
et al
.,

2009).


Fig.
3. Present status of global drifters as on November 09, 2009 (red
-

only SST; blue
-
SST and
air pressure; yellow
-

SST, air pressure and wind; green
-

SST, air pressure and salinity).


Fig. 4. Recommended hydrographic sections for the sustained decadal survey

(solid lines) and
high
-
frequency repeat lines (dashed lines).


Fig. 5. The Global tropical Moored Buoy Array in October 2009 (Source: McPhaden
et al
.
,
2009)


Fig. 6. The global distribution of Argo floats location
s

reporting on the Argo data system

as on
Dec 10, 2009
.


Fig. 7 Schematic chart of IndOOS . Fixed location in
-
situ observations of IndOOS are indicated
in detail, the argo and surface drifters scatter widely within the Indian Ocean, and the satellite
measurements cover surface observation
of

the w
hole area
.



44

Table. 1:
Strength
s

and
weaknesses

of different in
-
situ platforms

Platforms

Strengths

Weaknesses

Tide gauges

-
long term measurement

-
simple technology

-
easy to maintain

-
Only one parameter

-
along the coast

VOS

-
Surface marine met paramete
rs,


-
High resolution along repeat tracks

-
Sampling at remote oceanic region

-
Tracks not always where data
required

-
do not stop

-
no sub surface

SOOP

-
Temperature profile (760 m) and surface
salinity

-
High resolution along repeat tracks enabling
spati
al
-
time series

-
Sampling at remote oceanic region

-
deployment of autonomous sampling
platforms

-
Tracks not always where data
required

-
do not stop

Repeat
hydrography/

research vessel

-
Deploying sophisticated /heavy instruments

-

time series measurement
s of a many
parameters (physical, chemical, biological,
geological..)

-
reach remote areas, high resolution along the
repeat tracks

-
Inability to produce synoptic data
sets

-
very sparse sampling, expensive

Acoustic
tomography

-
Measuring and understanding

the behavior of
meso scale and large scale features associated
with General circulation

-
space
-
time variability

-
Temperature, heat content and
other variables are interpreted
with technique

-
not measured directly

Surface drifters

-
Horizontal spatial dom
ain from meters to the
basin
-
scale

-

data from remote regions

-
global coverage

Biofouling,

-
data storage volume for
telemetry


m
easurements only at the surface


45

-
rapid sampling in time


-
low
-
cost

-
robust technology

-

avoid some regions

-

limited variabl
es

Moorings

-
Inter disciplinary time series data to measure
changes in the ocean on time scales from
minutes to years

-
coastal and open ocean

-

to sample at multiple depths

-

harsh environment

-
real
-
time data availability,


-
Cannot provide horizontal
spatial
information

-
mixed temporal
-
spatial
variability is measured and thus
partitioning of local versus
advective effects requires
complementary spatial data sets

-

Vandalism

Floats

-
Horizontal spatial domain from meters to the
basin
-
scale

-
data from
remote regions

-

sub
-
surface information

-
rapid sampling in time

-
robust technology



-
low cost and hence large numbers feasible

-
Biofouling

-
profiling frequency,

-
data storage volume for
telemetry

-
coars
e

X,Y, T resolution

-
limited variables

-
avoid

some regions

Gliders

-
Good sampling along tracks

-
free choice of track

-
can be steered

-
different

sensor suite feasible

-
Very slow speed

-
limited depth range and variables

HF Radar

-
Good x,y,t resolution near the coast

-
land based

-
Expensive

-

limited variables and places


-
limited coverage


only surface
currents and wave



46




Fig. 1.











47




Fig. 2
.





48


Fig. 3.




Fig. 4.





49



Fig. 5



Fig. 6








50


Fig. 7